SOCIAL PROTECTION
1Gustave Nébié
INTRODUCTION
UNICEF West and Central Africa Region (WCAR) comprises 24 countries2 with very different levels of economic and social development. Within these 24 countries, some are part of the World Bank middle-income countries, while others are low-income countries. Other countries in the region are, however, classified by the World Bank or the Organization for Economic Cooperation and Development (OECD) as fragile countries, or as Least Developed Countries by the United Nations, or as Highly Indebted Poor Countries. Some are rich in natural resources, others are very poor, and an important feature of almost all these countries is weak social indicators, especially those relating to the welfare of children.
It is therefore important to better understand the underlying dynamics of this variegated situation, in order to adapt interventions and recommendations accordingly and for policies to be more efficient and equitable. The main objective of this chapter is to group countries in the region into relatively homogeneous sub-groups, based on the most recently measured key economic and social indicators, and draw operational lessons in terms of strategic planning and priority areas of interventions regarding social protection.
1 This chapter is a revised version of a paper entitled: “Clustering countries in West and Central Africa for improved UNICEF engagement in the region”, which is more general in terms of policy recommendations and in dealing with social and economic policies.
2 Benin, Burkina Faso, Cabo Verde, Cameroon, Central African Republic, Chad, Congo, Congo DR, Côte d’Ivoire, Equatorial Guinea, Gabon, Gambia, Ghana, Guinea, Guinea Bissau, Liberia, Mali, Mauritania, Niger, Nigeria, São Tomé e Príncipe, Senegal, Sierra Leone and Togo
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EXISTING METHODOLOGIES FOR CLASSIFICATION OF COUNTRIES There are many organizations that classify countries according to various criteria and for various reasons. We do not pretend to examine in a fully comprehensive way all existing country-classification methodologies but instead, we are just focusing on the most common. The idea is to see how countries in WCAR are positioning themselves vis-a-vis the others according to the different types of classification, and to examine if there is a general pattern that is appearing.
Box 1: Measuring the size of economies
Source: OECD 2015
There are many ways to measure the size and performance of an economy. The relative size of economies can be a useful measure, depending on the specific indicator and the method used to convert local currencies to US dollars. The following are most commonly used:
1. World Bank Atlas method
The World Bank’s official estimates of the size of economies are based on GNI converted to current US dollars using the Atlas method. The Atlas method smoothes exchange rate fluctuations by using a three-year moving average, price-adjusted conversion factor.
2. Purchasing Power Parities (PPP)
Purchasing power parity (PPP) conversion factors take into account differences in the relative prices of goods and services ― particularly non-tradables ― and therefore provide a better overall measure of the real value of output produced by an economy compared to other economies.
PPP GNI is measured in current international dollars which, in principle, have the same purchasing power as a dollar spent in the US economy. Because PPPs provide (in theory) a better measure of the standard of living of residents of an economy, they are often used for inter-country comparisons.
3. Market exchange rates
The total Gross Domestic Product (GDP) data are measured in current US dollars using annual market exchange rates. This means that the values and derived rankings are subject to greater volatility due to variations in exchange rates.
Inter-country comparisons based on GDP at market prices should, therefore, be treated with caution.
Clustering Countries in West and Central Africa 69 Classification according to GNI
The World Bank income per capita is widely used to classify countries. It is based on one indicator, the GNI (Gross National Income) per capita. The classification of countries by the World Bank is for the sake of its own lending procedures, but many other organizations are using it to compare countries and even to determine their own support policy to countries.
Table 1: Comparing GNI methods
Gross national income per capita 2014, Atlas method and PPP (USD)
rank3 Atlas PPP
7 São Tomé and Príncipe 1,600 3,030
8 Côte d'Ivoire 1,550 3,350
24 Central African Republic 330 610
Source: World Bank
The ranking is the same according to the two methods for the top 6 countries. On average, there is no big differences in the ranking of countries according to the two methods, but the difference in the size of
3 The rank is based on the Atlas method ranking
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the GNI per capita is huge (almost doubling from the Atlas method to the PPP method). Therefore, one should be very cautious when comparing GNI per capita across countries and make sure the same method is used to compare.
According to the latest classification (2014), WCAR countries are divided as follows:
Table 2: Classification of WCAR countries (2014) Low income countries
(less than $1046) Middle income countries High-income countries
Chad Cabo Verde Gabon Equatorial
Guinea children. The graph below (Graph 1) shows the level of GNI as compared to the level of child mortality. As we can see, Equatorial Guinea, the only high-income country in the region4, has a child mortality level that is among the worst in the region. Low-income countries, such as Togo or the Gambia, are performing much better than Equatorial Guinea in that area.
4 According to the latest available data at the time of writing from the World Bank in 2016, Equatorial Guinea moved back to the status of upper middle-income country.
Clustering Countries in West and Central Africa 71 We estimate a trend line using a polynomial function of order 25, and it seems to have a downward slope for child mortality for countries in the low-income to lower middle-income range (with a few exceptions, for example Nigeria). However, the slope is positive at higher levels of income per capita, (upper middle-income or higher). This is a worrying trend, particularly for richer countries. However, in view of the size of the sample (only one upper middle-income country and one high-income country), and also because of the dispersion around the trend line (e.g.
comparing Senegal or Cabo Verde with Nigeria or Gambia with Chad), we should be careful in interpreting these results.
Graph 1: Level of GNI per capita compared to child mortality
Source: World Bank & UNICEF
5 After testing many functions, an order 2 polynomial function seems to best fit the general trend.
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Classification according to the level of poverty a) Monetary Poverty
The national poverty line is the minimum income/consumption level used in a country for determining the proportion of population living in monetary poverty. Those households, whose income/consumption is less than this minimum, are classified as poor households, while households whose income/consumption is equal to or more than this threshold are considered non-poor. Based on this income threshold, a national poverty incidence is determined, which is the percentage of poor in the total population.
Graph 2: National poverty incidence
Source: UNDP HDR 2014
b) Multidimensional Poverty
Poverty is not just the absence of income. It is also the multiple consequences of this absence that are simultaneously experienced by people in poverty. Some of these consequences ― the non-monetary dimensions of poverty ― serve to prolong poverty and can become causes of its perpetuation. This definition of multidimensional poverty embraces a diverse range of characteristics such as material deprivation, social exclusion, lack of basic needs and rights, etc.
The Multidimensional Poverty Index (MPI) identifies multiple deprivations at the household and individual level in health, education
100 2030 4050 6070 8090
Cabo Verde Ghana Gabon Benin Cameroon Mauritania Côte d'Ivoire Mali Nigeria Congo Burkina Senegal Gambia Sierra Leone Guinea Chad Togo Niger São Tomé Liberia Guinea Bissau CAR Congo DR Equatorial Guinea
Clustering Countries in West and Central Africa 73 and standard of living6. It uses micro data from household surveys. Each person in a given household is classified as poor or non-poor depending on the number of deprivations his or her household experiences. This data are then aggregated into the national measure of poverty. The MPI attempts to reflect both the prevalence of multidimensional deprivation, and its intensity ― how many deprivations people experience at the same time with a single, dimension-less number. It can be used to create a comprehensive picture of households living in poverty, and permits comparisons both across countries, regions and the world, and within countries by ethnic group, urban or rural location, as well as other key household and community characteristics.
Dimensions included in the MPI are education, health, and living standards. All are equally weighted by one-third each and the scale of the index is from 0 to 1, 0 being no deprivation and 1 meaning maximum deprivation.
Education indicators are a) school attendance for school-age children; and b) school attainment for household members.
Health indicators are: (a) child mortality; and (b) nutrition.
Living Standards indicators include:
household access to electricity
household access to improved drinking water sources
household access to improved sanitation
household use of solid fuel for cooking and heating
existence of a finished floor in the house
existence of other assets that: (1) allow access to information (radio, TV, telephone); (2) support mobility (bike, motorbike, car, truck, animal cart, motorboat); and (3) support livelihood (refrigerator, agricultural land, livestock)
A household is not considered deprived in assets if it has at least one asset from group (1) and at least one asset from groups (2) or (3). A household is considered multidimensionally poor (or MPI poor) if the total of weighted deprivations (deprivation score) is equal to 1/3 or more.
6 UNDP’s Multidimensional Poverty Index: 2014. Specifications Kovacevic and Calderon (2014).
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A household is considered severely multidimensionally poor if the deprivation score is 1/2 or more.
A household is considered near-MPI poor if the deprivation score is 1/5 or more but less than 1/3.
A household is considered deprived but not near-MPI poor if the deprivation score is positive but less than 1/5.
If a household is deprived, then all its members are deprived.
Graph 3: MPI Multidimensional Poverty Incidence
Source: UNDP HDR 2014
For the MPI, three countries in our region do not have data: Cabo Verde, Chad, and Equatorial Guinea, which limits our capacity to compare countries in the region. Furthermore, data used to compare countries vary considerably; for some countries like Benin, data date back to 2006, and for others like Côte d’Ivoire, we have access only to the 2012 data. In general, data are old (2010 in most cases), which for some countries, such as CAR, may have changed considerably in view of the situation of the country today. Considering these limitations, comparing countries using this indicator may not be very appropriate.
Classification according to the level of governance a) Mo Ibrahim Governance Index
The Ibrahim Index of African Governance (IIAG), produced by the Mo Ibrahim Foundation, measures the quality of governance in every African country. It does this by compiling data from diverse global sources to
0 20 40 60 80 100
Gabon Ghana Congo Nigeria São Tomé Cameroon Togo Côte d'Ivoire Gambia Mauritania Senegal Benin Sierra Leone Congo DR CAR Guinea Bissau Liberia Burkina Mali Guinea Niger
Multidimensional Poverty incidence %
Clustering Countries in West and Central Africa 75 build an accurate and detailed picture of governance performance in African countries. Published annually, the IIAG provides a comprehensive assessment of governance performance for each of the 54 African countries. The 2015 IIAG consists of 93 indicators which fall into four categories: Safety & Rule of Law; Participation & Human Rights;
Sustainable Economic Opportunity; and Human Development. Countries are rated from 0 to 100, with 100 the best possible score.
Cabo Verde is the best performing country in the region, with an index of 74.5, while CAR has the lowest score, 24.9. It appears that there is no relationship between the level of GNI per capita and the quality of governance. For instance, Equatorial Guinea and the Republic of Congo, two countries with the highest per capita income, are among the worst six countries in the region in terms of Governance. On the other end, Benin and Burkina, two low income countries, are among the six best
The World Bank CPIA (Country Policy and Institutional Assessment) exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More
74,5 67,362,4
59,1 58,8 52,2 52,2
51,0 50,750,5 48,7 48,4 48,448,3 45,9 44,943,7 43,0 42,8
35,7 35,5 33,9 32,8
Cabo Verde Ghana Senegal São Tomé Benin Burkina Gabon Sierra Leone Liberia Gambia Mali Niger Togo Côte d'Ivoire Cameroon Nigeria Guinea Mauritania Congo Guinea Bissau Equatorial Guinea Congo DR Chad CAR
Level of IIAG
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specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance.
The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management; Structural Policies; Policies for Social Inclusion and Equity; and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 (low) to 6 (high).
The scores depend on the level of performance in a given year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the manner in which such actions should be factored into the ratings is carefully assessed, because in the end it is the implementation of legislation that determines the extent of its impact.
Unfortunately for this indicator, we do not have data for Gabon and Equatorial Guinea, the two highest ranking countries in term of GNI per capita. The lack of data to rank these two countries may be, by itself, an indication of the governance level in these countries.
Cabo Verde has the highest score, while CAR has the lower, as per the IIAG. Benin and Burkina are doing very well, as per the IIAG index.
Graph 5: CPIA index
Cabo Verde Senegal Burkina Benin Nigeria Ghana Mali Mauritania Niger Côte d'Ivoire Sierra Leone Cameroon Gambia Liberia São Tomé Congo Congo DR Guinea Togo Chad Guinea Bissau CAR
CPIA Index
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c) Transparency International Corruption Index
The Transparency International Corruption Index measures the perceived levels of public sector corruption on a scale of 0 (highly corrupt) to 100 (very clean). It is a composite index ― a combination of polls ― drawing on corruption-related data collected from a variety of institutions. The index reflects the views of observers from around the world, including experts living and working in the countries.
For Equatorial Guinea, there is no information. Cabo Verde is found to be the least corrupt country and Guinea Bissau the most corrupt, according to this index. One of the weaknesses of this index is that it is based on perception, which is not the same as being based on facts.
Graph 6: Transparency International Corruption Index 2014
Source: www.transparency.org Accessed February 2017
Classifications according to fragility a) OECD Fragile Countries
In order to promote debate and offer a fresh perspective, OECD presents a new tool for analysing fragility based on internationally agreed global priorities for reducing fragility and building resilience. It uses existing data to present five dimensions of fragility that relate directly to the UN’s 2015 Sustainable Development Goal. The five dimensions are as follows:
1. Violence: reduction of violence; 2. Justice: access to justice for all; 3.
Institutions: effective, accountable and inclusive institutions; 4. Economic
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Cabo Verde Ghana Senegal São Tomé Benin Burkina Gabon Liberia Niger Côte d'Ivoire Mali Sierra Leone Mauritania Gambia Togo Cameroon Nigeria Guinea CAR Congo Congo DR Chad Guinea Bissau
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foundations: economic foundations, inclusion and stability; 5. Resilience:
capacity to prevent and adapt to shocks and disasters.7
Fifteen countries in the region are then categorized as “fragile”
according to OECD methodology (in alphabetical order): Cameroon, CAR, Chad, Congo, Congo DR, Côte d’Ivoire, Guinea, Guinea Bissau, Liberia, Mali, Mauritania, Niger, Nigeria, Sierra Leone, Togo
b) World Bank Fragile Situations
The World Bank describes Fragile Situations as countries that have either a) a CPIA country rating of 3.2 or less; and/or b) the presence of a UN and/or regional peace keeping or peace building mission during the last three years. Therefore, the World Bank definition of fragile countries is strongly related to the countries’ governance score.
Based on that definition, nine countries in the region are defined as fragile by the World Bank:
CAR, Chad, Congo DR, Côte d’Ivoire, Guinea Bissau, Liberia, Mali, Sierra Leone, Togo
This list is much shorter than the OECD one. It is interesting to see that many countries that are categorized by OECD as fragile, are not defined as such according to the World Bank.
c) The Composite Index for Risk Management (InfoRM)
The INFORM initiative began in 2012 as a convergence of interests of UN agencies, donors, NGOs and research institutions to establish a common evidence-base for global humanitarian risk analysis.8
7 OECD (2015)
8 Organizations that make part of the INFORM initiative are: ACAPS (The Assessment Capacities Project) - is an initiative of a consortium of three NGOs (HelpAge International, Merlin and Norwegian Refugee Council); DFID (Department for International Development) is a United Kingdom government department; ECHO (Humanitarian Aid and Civil Protection department of the European Commission) - is the European Commission's department for overseas humanitarian aid and civil protection; FAO (Food and Agriculture Organization of United Nations); IASC (The Inter-Agency Standing Committee) is the primary mechanism for inter-agency coordination of humanitarian assistance. It is a unique forum involving the key UN and non-UN humanitarian partners; IOM (International Organization for Migration); OCHA (United Nations Office for the Coordination of Humanitarian Affairs); UNEP (United Nations Environment Programme); UNHCR (United Nations High Commissioner for Refugees); UNICEF (United Nation’s Children Fund); UNISDR (The United Nations Office for Disaster Risk Reduction); WFP (World Food Programme); WHO (World Health Organization)
Clustering Countries in West and Central Africa 79 INFORM identifies the countries at a high risk of humanitarian crisis that are more likely to require international assistance. The INFORM model is based on risk concepts published in scientific literature and envisages three dimensions of risk: hazards and exposure, vulnerability and lack of national coping capacity. The INFORM model is split into different levels to provide a quick overview of the underlying factors leading to humanitarian risk.
Table 3: InfoRM variables
Note: Risk = Hazard and exposure x vulnerability x lack of coping capacity Source: www.inform-index.org
Accessed February 2017
Ranking level Concept level (dimensions) Functional level (Categories) Component level
Earthquake Tsunami Floods Tropical Cyclone Drought
Current conflict Intensity Projected Conflict Risk Development and deprivation (50%)
Inequality (25%) Aid Dependency (25%) Uprooted people Other vulnerable groups Disaster Risk Reduction Governance
Communication Physical infrastructure Access to Health Systems
Lack of coping capacityHazard and exposureVulnerability
INFORM InfrastructureInstitutionalVulnerable groupsSocio-economicHumanNatural
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The INFORM index presents a scale of 0 to 10, 0 being no risk and 10 is maximum risk. INFORM does not distinguish countries between fragile and non-fragile ones. It merely ranks these countries from the least vulnerable to the most vulnerable. It then splits all countries into quartiles according to their level of vulnerability: low, medium, high and very high
The INFORM index presents a scale of 0 to 10, 0 being no risk and 10 is maximum risk. INFORM does not distinguish countries between fragile and non-fragile ones. It merely ranks these countries from the least vulnerable to the most vulnerable. It then splits all countries into quartiles according to their level of vulnerability: low, medium, high and very high